Convolutional-neural-network-based classifier and classifying method and training methods for the same

ABSTRACT

The present invention relates to a convolutional-neural-network-based classifier, a classifying method by using a convolutional-neural-network-based classifier and a method for training the convolutional-neural-network-based classifier. The convolutional-neural-network-based classifier comprises: a plurality of feature map layers, at least one feature map in at least one of the plurality of feature map layers being divided into a plurality of regions; and a plurality of convolutional templates corresponding to the plurality of regions respectively, each of the convolutional templates being used for obtaining a response value of a neuron in the corresponding region.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the foreign priority benefitof Chinese Patent Application No. 201310329465.7, filed Jul. 31, 2013,the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to the field of image recognition, andparticularly to a convolutional-neural-network-based classifier, aclassifying method by using a convolutional-neural-network-basedclassifier and a method for training theconvolutional-neural-network-based classifier.

BACKGROUND ART

A convolutional neural network (referred to as CNN for short) classifieris generally used for image recognition. FIG. 1 illustrates aconventional convolutional-neural-network-based classifier. FIGS. 4-7illustrate a process of calculating a response value of a neuron using aconventional convolutional-neural-network-based classifier. In theprocess of performing recognition using the conventionalconvolutional-neural-network-based classifier, taking hand-writtendigits for example, an image is inputted, and after a plurality of timesof repeated operations of convolution, space max sampling and fullconnection are performed, the convolutional-neural-network-basedclassifier outputs confidence on each kind of digit, and an output withthe highest confidence is a recognition result. Each box in FIG. 1 andFIGS. 4 to 7 is a feature map, for example F1, F2 and F3, and an inputimage may also be regarded as a feature map.

In the above method, the convolution operation performs a function ofextracting a feature from a feature map in a previous layer. However,the convolution operation of the conventionalconvolutional-neural-network-based classifier limits the number ofextractable features to a great extent, resulting in an insufficientlyflexible structure of the convolutional-neural-network-based classifier.

SUMMARY OF THE INVENTION

Hereinafter, brief descriptions of the present invention will be made,so as to provide a basic understanding to some aspects of the presentinvention. It should be understood that the brief descriptions are notexhaustive brief descriptions of the present invention. They are neitherintended to determine the key or essential parts of the presentinvention nor intended to limit the scope of the present invention, butonly aim to provide some concepts in a simplified form, for use as apreamble of more detailed descriptions addressed later.

A main object of the present invention is to provide aconvolutional-neural-network-based classifier, a classifying method byusing a convolutional-neural-network-based classifier and a method fortraining the convolutional-neural-network-based classifier.

According to one embodiment of the present invention, there is provideda convolutional-neural-network-based classifier, comprising: a pluralityof feature map layers, at least one feature map in at least one of theplurality of the feature map layers being divided into a plurality ofregions; and a plurality of convolutional templates corresponding to theplurality of regions respectively, each of the convolutional templatesbeing used for obtaining a response value of a neuron in thecorresponding region.

According to another embodiment of the present invention, there isprovided a classifying method by using aconvolutional-neural-network-based classifier which comprises aplurality of feature map layers, comprising: dividing at least onefeature map in at least one of the plurality of the feature map layersinto a plurality of regions; performing forward propagation by inputtingthe an object to be classified into theconvolutional-neural-network-based classifier to obtain an outputresult, during the forward propagation, each of the convolutionaltemplates, which correspond to the plurality of regions respectively, isused for obtaining a response value of a neuron in the correspondingregion; and classifying the object to be classified according to theoutput result.

According to yet another embodiment of the present invention, there isprovided a method for training the convolutional-neural-network-basedclassifier, which comprises a plurality of feature map layers,comprising: dividing at least one feature map in the at least one of theplurality of the feature map layers into a plurality of regions;performing forward propagation by inputting a training sample with aknown flag into the convolutional-neural-network-based classifier toobtain an output result, during the forward propagation, each of theconvolutional templates, which correspond to the plurality of regionsrespectively, is used for obtaining a response value of a neuron in thecorresponding region; performing back propagation according to thedifference between the output result and the known flag to correctparameters of the convolutional-neural-network-based classifiercomprising the weights in the convolutional template; and repeating theabove steps until a predetermined condition is met.

In addition, the embodiments of the present invention further provide acomputer program for carrying out the above method.

Besides, the embodiments of the present invention further provide acomputer product program in the form of at least a computer-readablemedium.

These and other advantages of the present invention will become moreapparent from the following detailed descriptions of the bestembodiments of the present invention combined with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Hereinafter, embodiments of the present invention will be describedcombined with the attached drawings in order to facilitate theunderstanding to the above and other advantages and features of thepresent invention. The components in the attached drawings aim only toillustrate the principle of the present invention. In the attachedDrawings, identical or similar technical features or components will bedenoted by identical or similar reference signs.

FIG. 1 is a schematic view illustrating a conventionalconvolutional-neural-network-based classifier;

FIG. 2 is a schematic view illustrating aconvolutional-neural-network-based classifier according to an embodimentof the present invention;

FIG. 3 is a schematic view illustrating parts selected from FIG. 1 or 2;

FIGS. 4 to 7 are schematic views illustrating a process of calculating aresponse value of a neuron using the conventionalconvolutional-neural-network-based classifier;

FIGS. 8 to 11 are schematic views illustrating a process of calculatinga response value of a neuron using theconvolutional-neural-network-based classifier according to theembodiment of the present invention;

FIG. 12 is a schematic view illustrating a dividing scheme of featuremaps in the convolutional-neural-network-based classifier according tothe embodiment of the present invention;

FIG. 13 is a flowchart illustrating a method for training aconvolutional-neural-network-based classifier according to an embodimentof the present invention;

FIG. 14 is a flowchart illustrating a classifying method performed by aconvolutional-neural-network-based classifier according to an embodimentof the present invention; and

FIG. 15 is an exemplary structural view of a computing device that canbe used for carrying out the classifying method performed by aconvolutional-neural-network-based classifier and the method fortraining the convolutional-neural-network-based classifier according tothe present disclosure.

DETAILED DESCRIPTION OF THE EMBODIEMENTS

Hereinafter, embodiments of the present invention will be described withreference to the attached drawings. Elements and features described inone figure or one embodiment of the present invention may be combinedwith elements and features illustrated in one or more other figures orembodiments. It should be noted that, for the purpose of clarity,representations and descriptions of components and processing irrelevantto the present application which are known to those ordinarily skilledin the art are omitted in the attached drawings and descriptionsthereof.

FIG. 2 is a schematic view illustrating aconvolutional-neural-network-based classifier according to an embodimentof the present invention. FIG. 3 is a schematic view illustrating partsselected from FIG. 1 or 2. FIGS. 8 to 11 are schematic viewsillustrating a process of calculating a response value of a neuron usingthe convolutional-neural-network-based classifier according to theembodiment of the present invention.

As shown in FIG. 2, FIG. 3 and FIGS. 8-11, theconvolutional-neural-network-based classifier according to theembodiment of the present invention comprises a plurality of feature maplayers, at least one feature map in at least one of the feature maplayers being divided into a plurality of regions. Theconvolutional-neural-network-based classifier according to theembodiment of the present invention further comprises a plurality ofconvolutional templates corresponding to the plurality of regionsrespectively, each of the convolutional templates being used forobtaining a response value of a neuron in the corresponding region.

Specifically, comparing the convolutional-neural-network-basedclassifier as shown in FIG. 2 with the conventionalconvolutional-neural-network-based classifier as shown in FIG. 1,less-constrained convolution operations 1′ an 2′ in FIG. 2 substitutefor convolution operations 1 and 2 in FIG. 1, respectively. In addition,as compared with FIG. 1, in FIG. 2, a less-constrained convolutionoperation 3′ is added prior to the full-connection operation. Theless-constrained convolution operation in FIG. 2 is the aforementionedoperation of performing convolution using a plurality of convolutionaltemplates.

In this text, the term “less-constrained convolution operation” refersto using different convolutional templates in calculating responsevalues of neurons in the same feature map, wherein a layer where thefeature map lies is called a “less-constrained convolution layer”.

In other embodiments, a network structure of theconvolutional-neural-network-based classifier may be properly adjusted.For example, the number of paired operations of “less-constrainedconvolution-space max sampling” is increased or decreased, the number ofthe full-connection operation is increased or decreased, the number ofthe less-constrained convolution operation appearing separately isincreased or decreased, the space max sampling operation is substitutedwith a space sub sampling operation, etc.

To further describe the details of the less-constrained convolutionoperation, only parts of related features maps F1, F2 and F3 in FIG. 2are selected to make exemplary descriptions. For the parts selected fromFIG. 2, please refer to FIG. 3. As shown in FIG. 3, two adjacent layers,Layer L and layer L+1, are selected. Hereinafter, the conventionalconvolutional-neural-network-based classifier and theconvolutional-neural-network-based classifier according to the presentinvention will be compared with each other on the basis of FIG. 3, so asto find differences therebetween.

FIGS. 4 to 7 illustrate a process of calculating response values (theresponse values are also correspondingly marked as T1, T2, T3 and T4) ofneurons T1, T2, T3, and T4 in the feature map F3 in the layer L+1 inFIG. 3 using the conventional convolutional-neural-network-basedclassifier. FIGS. 8 to 11 illustrate a process of calculating responsevalues (the response values are also correspondingly marked as T1, T2,T3 and T4) of neurons T1, T2, T3, and T4 in the feature map F3 in thelayer L+1 in FIG. 3 using the convolutional-neural-network-basedclassifier according to the embodiments of the present invention.Hereinafter, the convolutional-neural-network-based classifier accordingto the present invention will be described through comparison.

Firstly, in order to facilitate understanding, FIGS. 4 to 11 aredescribed as follows:

(1) Each circle in FIGS. 4 to 11 represents a neuron.

(2) All w values in FIGS. 4 to 11 are values of the convolutionaltemplates, and all the w values are learned through back propagationduring the training of the neural network.

(3) The size of all the convolutional templates in FIGS. 4 to 11 is 2×2;however, it would be understood that the size of the convolutionaltemplates may be of any value in actual use.

(4) The dashed line in the feature map F3 in the layer L+1 in FIGS. 8 to11 indicates that all the neurons in the feature map F3 are divided intotwo classes, wherein each class of the neurons uses identical templates,that is, T1 and T2 use one group of convolutional templates, and T3 andT4 use another group of convolutional templates. As would be readilyunderstood, however, the number of classes to which neurons in onefeature map may be arbitrarily specified, and the manner of the dividingthereof may be determined according to specific tasks. For example, forhandwritten characters recognition, dividing may be performed accordingto the stroke pixel density distribution. Various dividing schemes willbe described below.

(5) Specific formulae for calculating the response values of the neuronsT1, T2, T3 and T4 are provided at the lower part in FIGS. 4 to 11. Theprocess of calculating the response values of the neurons in the layerL+1 is obtained by applying a slide window at the layer L, andmultiplying the response values of the neurons in the slide window ofthe layer L by the corresponding values of the convolutional templates.In the formulae, b, b1 and b2 represent bias terms (learning isperformed through back propagation during the training of the neutralnetwork). In the formulae, function Func( ) represents a nonlineartransformation function, which uses a hyperbolic tangent function in aconventional method, and according to the embodiment of the presentinvention, a non-linear truncated function may be used to achieve theobject of increasing a convergence speed, which will be described later.

Firstly, the process of calculating response values (the response valuesare also correspondingly marked as T1, T2, T3 and T4) of neurons T1, T2,T3, and T4 in the feature map F3 in the layer L+1 in FIG. 3 using theconventional convolutional-neural-network-based classifier is describedwith reference to FIGS. 4-7.

FIG. 4 illustrates the process of calculating the response value of theneuron T1 in the feature map F3 in the layer L+1 in FIG. 3 using theconventional convolutional-neural-network-based classifier. As shown inFIG. 4, in the conventional convolutional-neural-network-basedclassifier, the response value of the neuron T1 in the feature map F3 isobtained by using response values (the response values are alsocorrespondingly marked as S1, S2, S4 and S5) of neurons S1, S2, S4 andS5 (i.e. the four neurons at the left upper part of the feature map F1)in the feature map F1 and response values (the response values are alsocorrespondingly marked as S10, S11, S13 and S14) of neurons S10, S11,S13 and S14 (i.e. the four neurons at the left upper part of the featuremap F2) in the feature map F2 in the layer L, as well as a templatecomprising weights w1, w2, w3 and w4 and a template comprising w5, w6,w7 and w8. Therefore, the response value T1 is as follows:

T1=Func(w1*S1+w2*S2+w3*S4+w4*S5+w5*S10+w6*S11+w7*S13+w8*S14)+b  (1)

FIG. 5 illustrates the process of calculating the response value of theneuron T2 in the feature map F3 in the layer L+1 in FIG. 3 using theconventional convolutional-neural-network-based classifier. As shown inFIG. 5, in the conventional convolutional-neural-network-basedclassifier, the response value of the neuron T2 in the feature map F3 isobtained using response values (the response values are alsocorrespondingly marked as S2, S3, S5 and S6) of neurons S2, S3, S5 andS6 (i.e. the four neurons at the right upper part of the feature map F1)in the feature map F1 and response values (the response values are alsocorrespondingly marked as S11, S12, S14 and S15) of neurons S11, S12,S14 and S15 (i.e. the four neurons at the right upper part of thefeature map F2) in the feature map F2 in the layer L, as well as atemplate comprising weights w1, w2, w3 and w4 and a template comprisingw5, w6, w7 and w8. Therefore, the response value T2 is as follows:

T2=Func(w1*S2+w2*S3+w3*S5+w4*S6+w5*S11+w6*S12+w7*S14+w8*S15)+b  (2)

FIG. 6 illustrates the process of calculating the response value of theneuron T3 in the feature map F3 in the layer L+1 in FIG. 3 by using theconventional convolutional-neural-network-based classifier. As shown inFIG. 6, in the conventional convolutional-neural-network-basedclassifier, the response value of the neuron T3 in the feature map F3 isobtained by using response values (the response values arecorrespondingly marked as S4, S5, S7 and S8) of neurons S4, S5, S7 andS8 (i.e. the four neurons at the left lower part of the feature map F1)in the feature map F1 and response values (the response values arecorrespondingly marked as S13, S14, S16 and S17) of neurons S13, S14,S16 and S17 (i.e. the four neurons at the right upper part of thefeature map F2) in the feature map F2 in the layer L, as well as atemplate comprising weights w1, w2, w3 and w4 and a template comprisingw5, w6, w7 and w8. Therefore, the response value T3 is as follows:

T3=Func(w1*S4+w2*S5+w3*S7+w4*S8+w5*S13+w6*S14+w7*S16+w8*S17)+b  (3)

FIG. 7 illustrates the process of calculating the response value of theneuron T4 in the feature map F3 in the layer L+1 in FIG. 3 by using theconventional convolutional-neural-network-based classifier. As shown inFIG. 7, in the conventional convolutional-neural-network-basedclassifier, the response value of the neuron T4 in the feature map F3 isobtained by using response values (the response values arecorrespondingly marked as S5, S6, S8 and S9) of neurons S5, S6, S8 andS9 (i.e. the four neurons at the right lower part of the feature map F1)in the feature map F1 and response values (the response values arecorrespondingly marked as S14, S15, S17 and S18) of neurons S14, S15,S16 and S18 (i.e. the four neurons at the right upper part of thefeature map F2) in the feature map F2 in the layer L, as well as atemplate comprising weights w1, w2, w3 and w4 and a template comprisingw5, w6, w7 and w8. Therefore, the response value T4 is as follows:

T4=Func(w1*S5+w2*S6+w3*S8+w4*S9+w5*S14+w6*S15+w7*S17+w8*S18)+b  (4)

As can be seen from FIGS. 4 to 7 (in particular the calculation formulaeprovided therein), for the conventional convolutional manner, incalculating a response value of any neuron in the feature map F3 in thelayer L+1: the feature map F1 in the layer L uses the same convolutionaltemplates (w1, w2, w3, w4) all the time, and the feature map F2 in thelayer L uses the same convolutional templates (w5, w6, w7, w8) all thetime.

Now, the process of calculating the response values of the neurons T1,T2, T3 and T4 in the feature map F3 in the layer L+1 in FIG. 3 by usingthe convolutional-neural-network-based classifier according to thepresent invention is described with reference to FIGS. 8 to 11.

As shown in FIGS. 8 to 11, the feature map F3 is divided into upper andlower regions, wherein the response values of the neurons T1 and T2 inthe upper region are calculated using one group of templates, while theresponse values of the neurons T3 and T4 in the lower region arecalculated using another group of templates.

FIG. 8 illustrates the process of calculating the response value of theneuron T1 in the feature map F3 in the layer L+1 in FIG. 3 by using theconvolutional-neural-network-based classifier according to the presentinvention. As shown in FIG. 8, in the convolutional-neural-network-basedclassifier according to the embodiments of the present invention, theresponse value of the neuron T1 in the feature map F3 is obtained byusing response values (the response values are correspondingly marked asS1, S2, S4 and S5) of neurons S1, S2, S4 and S5 (i.e. the four neuronsat the left upper part of the feature map F1) in the feature map F1 andresponse values (the response values are correspondingly marked as S10,S11, S13 and S14) of neurons S10, S11, S13 and S14 (i.e. the fourneurons at the left upper part of the feature map F2) in the feature mapF2 in the layer L, as well as a template comprising weights w1, w2, w3and w4 and a template comprising w5, w6, w7 and w8. Therefore, theresponse value T1 is as follows:

T1=Func(w1*S1+w2*S2+w3*S4+w4*S5+w8*S10+w6*S11+w7*S13+w8*S14)+b1  (5)

FIG. 9 illustrates the process of calculating the response value of theneuron T2 in the feature map F3 in the layer L+1 in FIG. 3 by using theconvolutional-neural-network-based classifier according to theembodiments of the present invention. As shown in FIG. 9, in theconvolutional-neural-network-based classifier according to theembodiments of the present invention, the response value of the neuronT2 in the feature map F3 is obtained by using response values (theresponse values are correspondingly marked as S2, S3, S5 and S6) ofneurons S2, S3, S5 and S6 (i.e. the four neurons at the right upper partof the feature map F1) in the feature map F1 and response values (theresponse values are correspondingly marked as S11, S12, S14 and S15) ofneurons S11, S12, S14 and S15 (i.e. the four neurons at the right upperpart of the feature map F2) in the feature map F2 in the layer L, aswell as a template comprising weights w1, w2, w3 and w4 and a templatecomprising w5, w6, w7 and w8. Therefore, the response value T2 is asfollows:

T2=Func(w1*S2+w2*S3+w3*S5+w4*S6+w5*S11+w6*S12+w7*S14+w8*S15)+b1  (6)

It can be seen that the same set of templates is used in calculating theresponse values of the neurons T1 and T2 in the upper region of thefeature map F3.

FIG. 10 illustrates the process of calculating the response value of theneuron T3 in the feature map F3 in the layer L+1 in FIG. 3 by using theconvolutional-neural-network-based classifier according to theembodiments of the present invention. As shown in FIG. 10, in theconvolutional-neural-network-based classifier according to the presentinvention, the response value of the neuron T3 in the feature map F3 isobtained by using response values (the response values arecorrespondingly marked as S4, S5, S7 and S8) of neurons S4, S5, S7 andS8 (i.e. the four neurons at the left lower part of the feature map F1)in the feature map F1 and response values (the response values arecorrespondingly marked as S13, S14, S16 and S17) of neurons S13, S14,S16 and S17 (i.e. the four neurons at the right upper part of thefeature map F2) in the feature map F2 in the layer L, as well as atemplate comprising weights w9, w10, w11 and w12 and a templatecomprising w13, w14, w15 and w16. Therefore, the response value T3 is asfollows:

T3=Func(w9*S4+w10*S5+w11*S7+w12*S8+w13*513+w14*S14+w15*S16+w16*S17)+b2  (7)

FIG. 11 illustrates the process of calculating the response value of theneuron T4 in the feature map F3 in the layer L+1 in FIG. 3 using theconvolutional-neural-network-based classifier according to theembodiments of the present invention. As shown in FIG. 11, in theconvolutional-neural-network-based classifier according to theembodiments of the present invention, the response value of the neuronT4 in the feature map F3 is obtained by using response values (theresponse values are correspondingly marked as S5, S6, S8 and S9) ofneurons S5, S6, S8 and S9 (i.e. the four neurons at the right lower partof the feature map F1) in the feature map F1 and response values (theresponse values are correspondingly marked as S14, S15, S17 and S18) ofneurons S14, S15, S17 and S18 (i.e. the four neurons at the right upperpart of the feature map F2) in the feature map F2 in the layer L, aswell as a template comprising weights w9, w10, w11 and w12 and atemplate comprising w13, w14, w15 and w16. Therefore, the response valueT4 is as follows:

T4=Func(w9*S5+w10*S6+w11*S8+w12*S9+w13*S14+w14*S15+w15*S17+w16*S18)+b2  (8)

As can be seen by comparing FIGS. 8 to 11 (in particular the calculationformulae provided therein), for the less-constrained convolutionoperation, in calculating a response value of any neuron in the featuremap F3 in the layer L+1: for the neurons T1 and T2 belonging to the sameclass, the feature map F1 in the layer L uses the same convolutionaltemplates (w1, w2, w3, w4), and the feature map F2 in the layer L usesthe same convolutional templates (w5, w6, w7, w8). For the neurons T3and T4 belonging to another class, the feature map F1 in the layer Luses the convolutional templates (w9, w10, w11, w12), and the featuremap F2 in the layer L uses the same convolutional templates (w13, w14,w15, w16). For the less-constrained convolution operation, incalculating response values of the respective neurons in the samefeature map, neurons belonging to the same class use the sameconvolutional templates.

In the above embodiment, by less-constraining the convolution operationin such a way that convolution is performed using a plurality ofconvolutional templates between two feature maps (for example, F3 isconvoluted with F1 using a plurality of templates), it is made possiblethat more features are extracted. According to mode recognition theory,a recognition effect of a statistical classifier depends mainly on thenumber and the validity of extracted features for a sample. The basicoperations of feature extraction using a plurality of templates andfeature extraction using a single template are consistent with eachother (that is, both are convolution), and more features can beextracted by using a plurality of templates, so compared with the mannerof using a single template, the method of using a plurality of templateshas an advantage in terms of improving classification accuracy.

In addition, according to the embodiment of the present application, anactivation function used in calculating the response values may beconfigured to acquire positive response values, so as to increase aconvergence speed.

The nonlinear transformation function Func( ) (also referred to as anactivation function) in FIGS. 8 to 11 is configured to convert anegative convolution result to a positive small value. A convolutionresult refers to contents in the parentheses of the function Func( ).For example, in calculating a response value of each neuron, a nonlineartruncated function is used as the nonlinear transformation functionFunc( ) in FIGS. 8-11, as shown in the following formula (9):

$\begin{matrix}{{{Func}(x)} = \left\{ \begin{matrix}ɛ & {{x \leq 0},{ɛ \in \left( {0,10^{- 6}} \right)}} \\x & {x > 0}\end{matrix} \right.} & (9)\end{matrix}$

Where x represents a convolution result. For example, in the formula(5), x represents

w1*S1w2*S2w3*S4w4*S5+w5*S10w6*S11w7*S13w8*S14

The above formula (9) means that all the values of x which are less thanor equal to 0 are truncated to a small quantity E which is slightlygreater than 0, while the values of x which are originally greater than0 are kept invariable. Hereinafter, setting the small quantity to bewithin a range of 0 to 10-6 is only an example; actually, the smallquantity may be set to be a value within other ranges according torequirements.

In another embodiment of the present invention, as the nonlineartransformation function Func( ) in FIGS. 8 to 11, a nonlinear truncatedfunction configured to take an absolute value for a negative convolutionresult may be used, as shown in the following formula (10):

Func(x)=|x|  (10)

As would be readily understood, the above formulae (9) and (10) are onlyexamples; and other nonlinear transformation functions may be usedaccording to requirements, as long as the functions are configured toacquire positive response values.

By using a new activation function, i.e. an activation functionconfigured to acquire positive response values, an increase in aconvergence speed is made possible. The less-constrained convolutionincreases the number of the extracted features, causing a great increasein the total number of parameters of the whole convolutional neutralnetwork, such that in a case where the total number of training samplesare invariable, during network training the convergence speed willdecrease, prolonging training time; using an activation function whichacquires positive response values can increase the convergence speed,which is very advantageous.

Hereinafter, the dividing scheme of feature maps in theconvolutional-neural-network-based classifier according to theembodiment of the present invention is described.

For the convolutional-neural-network-based classifier according to theembodiment of the present invention, in a training phase it is necessaryto pre-determine a dividing scheme of feature maps. In one embodiment,for the convoultional neural network classifier as shown in FIG. 2, adividing scheme of neurons on all the feature maps in the firstless-constrained convolutional layer counted from the left is determinedfirst, and then a dividing scheme of neurons on feature maps inremaining less-constrained convolutional layers.

A physical basis for determining a dividing schemes is described first.

The physical basis for dividing schemes may be related to specifictasks. For example, in one embodiment, dividing of a plurality ofregions (each region includes a predetermined number of neurons) in afeature map is performed based on the distribution of pixels of animage. Hereinafter, taking handwritten characters recognition forexample, a dividing scheme based on stroke pixel distribution isintroduced; a basic idea of this solution is seeking for a dividingscheme, so as to ensure a relatively small difference in the strokepixel distribution between divided regions. For the sake of conciseness,the dividing scheme of neurons on all the feature maps in the firstless-constrained convolutional layer counted from the left is introducedonly for the convolutional neural network as shown in FIG. 2, and allthe feature maps may adopt the same dividing scheme.

Assuming that there exists a total of N training samples, binarizationis performed for all the character samples imagek (k=1, N) used fortraining according to conventional methods, and then they are normalizedto the same size (with a height of H and a width of W). Thereafter, allthe character samples are added together in a point-to-point manner, soas to obtain a stroke pixel summarizing map StrokeMap:

$\begin{matrix}{{{{StrokeMap}\left( {i,j} \right)} = {\frac{1}{N} \cdot {\sum\limits_{k = 1}^{N}{{image}_{k}\left( {i,j} \right)}}}}{{i = 1},\ldots \;,{{H\mspace{14mu} j} = 1},\ldots \;,W}} & (11)\end{matrix}$

Since the binarization operation is performed, on the character samplesregions which are 1 represent presence of stroke pixels and regionswhich are 0 represent an absence of stroke pixels. A value of each pixelon the stroke pixel summarizing map StrokeMap is an average value ofnumbers of times of all the training images having stroke pixels at thepixel position.

All the pixels on the stroke pixel summarizing map StrokeMap aretraversed, with the following operations being performed:

(1) A current point (i, j) is determined on the stroke pixel summarizingmap StrokeMap.

(2) The stroke pixel summarizing map StrokeMap is divided into apredetermined number of regions, e.g. four regions, by means of ahorizontal line and a vertical line of this point, and a sum of averagevalues of numbers of times of stroke pixels in each of the regions (i.e.a sum of all the pixel values) is calculated.

(3) Each two of the four regions are divided as one group, and a totalof six groups (C₄ ²=6) may be divided. A difference in a sum of averagevalues of numbers of times of stroke pixels in two regions in each groupis calculated. A sum of differences of the six groups is calculated andrecorded.

A point with a minimum value of the sum of the differences of the sixgroups as recorded is sought for in the stroke pixel summarizing mapStrokeMap. The stroke pixel summarizing map StrokeMap is divided intofour regions by means of a horizontal line and a vertical line of thispoint, and this dividing scheme is used as a dividing scheme of neuronson a feature map, that is, all the neurons on the feature map alsocorrespond to four classes, with each class of neurons using the sameconvolutional templates.

In the above embodiment, the dividing of the plurality of regions of thefeature map is performed based on a pixel distribution of the image, andspecifically, is performed based on a quantity distribution offoreground pixels of a binary image of an image. As would be readilyunderstood, however, the dividing of the plurality of regions may alsobe performed based on a density distribution of foreground pixels of abinary image of the image. For example, each region as divided may bemade to have the same density.

In addition, in one embodiment, dividing of a plurality of regions maybe performed based on the structure of an image. Still taking acharacter image for example, for example an image to be divided is ahandwritten character “

”. The character “

” is of a left-right structure, and thus can be divided into left andright regions according to radicals, i.e. an image region containing “

” and an image region containing “

”. For example, an image to be divided is a handwritten character “6”,the character “6” may be divided into upper and lower parts, i.e. animage region generally containing “o” and an image region generallycontaining the upper circular arc part. It should be noted that: eachperson has a different handwriting habit, so during actual dividing, aproportion of an image to be divided is not fixed, but floats on thebasis of a predetermined proportion.

Although performing the dividing of the regions based on the structureof an image or a binary image of an image is described herein by takinga handwritten character for example, it would be readily understood thatthe targeted image is not limited to a handwritten character but may beany other proper images, such as an image of a splintering cell, a mapimage and so on.

The foregoing describes the physical basis for determining dividingschemes. Next, dividing schemes will be described according to whetherdividing schemes between a plurality of less-constrained convolutionallayers and between a plurality of feature maps are the same as eachother. There exist dividing schemes of two levels, a first one of whichis whether all the feature maps to which each sample corresponds use thesame dividing scheme, and a second one of which is whether the samedividing scheme is used between samples. As far as this is concerned,the following four dividing schemes may be used for all feature maps inone convolutional-neural-network-based classifier:

(1) All the samples use the same dividing scheme, wherein all thefeature maps to which each sample corresponds use the same dividingscheme. Herein, “all the feature maps” refer to all the feature maps ona less-constrained convolutional layer.

(2) All the samples use the same dividing scheme, wherein all thefeature maps to which each sample corresponds use different dividingschemes. Herein, “all the feature maps” refer to all the feature maps ona less-constrained convolutional layer. The case where all the featuremaps use different dividing schemes mainly covers the following twocases: (a) all the feature maps on each layer use the same dividingscheme, but dividing schemes used between layers are different; and (b)different dividing schemes are used between all the feature maps on eachlayer.

(3) All the samples use different dividing schemes, wherein all thefeature maps to which each sample corresponds use the same dividingscheme. Herein, “all the feature maps” refer to all the feature maps ona less-constrained convolutional layer.

(4) All the samples use different dividing schemes, wherein all thefeature maps to which each sample corresponds use different dividingschemes. Herein, “all the feature maps” refer to all the feature maps ona less-constrained convolutional layer. The case where all the featuremaps use different dividing schemes mainly covers the following twocases: (a) all the feature maps on each layer use the same dividingscheme, but dividing schemes used between layers are different; and (b)different dividing schemes are used between all the feature maps on eachlayer.

Hereinafter, one embodiment of the above scheme (4) of dividing will bedescribed by way of an example. FIG. 12 illustrates a process ofapplying three kinds of dividing schemes (red lines in FIG. 12) of aninput sample “6” to three less-constrained convolutional layersrespectively. All the samples use different dividing schemes, and allthe feature maps on each layer use the same dividing scheme, butdividing schemes used between layers are different.

The foregoing describes dividing schemes according to whether dividingschemes between a plurality of less-constrained convolutional layers andbetween a plurality of feature maps are the same. Hereinafter, dividingschemes will be described according to whether dividing schemes for aplurality of different samples are fixed.

In one embodiment, an image serving as a sample may be divided into aplurality of image regions according to a fixed scheme, and theplurality of image regions obtained by the dividing correspond to aplurality of regions of a feature map on a less-constrainedconvolutional layer respectively. Herein, for example, a fixed schememay be determined based on general features of training samples. Inother words, a fixed solution is determined based on general features oftraining samples, and then each training sample is divided according tothe fixed scheme. A specific fixed scheme may be any one of the aboveschemes.

In another embodiment, the convolutional-neural-network-based classifierfurther comprises a image dividing unit (not shown), which may divide animage serving as a sample into a plurality of image regions, and theplurality of image regions obtained by the dividing correspond to aplurality of regions on a less-constrained convolutional layerrespectively. Herein, a dynamic solution may be determined based onpersonal features of training samples. In other words, since it ispossible that personal features of one training sample are differentfrom personal features of another training sample, dividing schemes fordifferent training samples are possibly different. In this case, theimage dividing unit may discriminately divide the image serving as thesample into a plurality of image regions based on the personal featuresof the training samples. A specific dividing scheme may also be any oneof the above solutions.

Hereinafter, a method 1300 for training theconvolutional-neural-network-based classifier according to theembodiment of the present invention will be described with reference toFIG. 13.

As shown in FIG. 13, in Step S1302, at least one feature map in the atleast one of the plurality of feature map layers included in theconvolutional-neural-network-based classifier is divided into aplurality of regions.

For example, at least one feature map in the at least one layer may bedivided into a plurality of regions by using one of the above dividingschemes.

In addition, the layout of the convolutional-neural-network-basedclassifier according to the embodiment of the present invention isgiven; for example, the layout comprises the number of network layers,operations (less-constrained convolution, space max sampling,full-connection) on each layer, the number of feature maps and the sizeof feature maps on each layer, wherein the number of feature maps on anoutput layer is consistent with the class number of specific tasks. Forexample, for a digit recognition task, the number of feature maps of anoutput layer is 10, while for an English capital recognition task, thenumber of feature maps of an output layer is 26.

In Step S1304, forward propagation is performed by putting a trainingsample with a known flag into the convolutional-neural-network-basedclassifier to obtain an output result, during the forward propagation,and each of the convolutional templates, which correspond to theplurality of regions respectively, is used for obtaining a responsevalue of a neuron in the corresponding region. Preferably, the responsevalue may be calculated by using an activation function configured toacquire a positive response value. For example, the activation functionmay be an activation function configured to take an absolute value for anegative convolutional result or to convert a negative convolutionalresult to a positive small value.

For example, for weights of all the templates in the network, randominitialization may be performed by using 0-1 distribution. Then, all thetraining samples are sequentially fed into theconvolutional-neural-network-based classifier according to theembodiment of the present invention to perform training. For each of thetraining samples, an output result is obtained according to the forwardpropagation, i.e. a value of an output layer.

In Step S1306, back propagation is performed according to the differencebetween the output result and the known flag to correct parameters ofthe convolutional-neural-network-based classifier comprising the weightsin the convolutional template. In other words, the parameters of theconvolutional-neural-network-based classifier not only comprise theweights in the convolutional template but also comprise otherparameters.

In Step S1308, it is judged whether a predetermined condition is met.For example, it is judged whether an error rate of theconvolutional-neural-network-based classifier according to theembodiment of the present invention on a training set reaches or islower than a predetermined value. However, it would be readilyunderstood that the predetermined condition may also be otherconditions, such as a predetermined time number or other convergenceconditions and so on.

If the predetermined condition is met in Step S1308, processing ends;otherwise, return to Step S1302.

Hereinafter, a classifying method 1400 performed by aconvolutional-neural-network-based classifier according to theembodiment of the present invention will be described with reference toFIG. 14.

As shown in FIG. 14, in Step S1402, at least one feature map in the atleast one of the plurality of feature map layers included in theconvolutional-neural-network-based classifier is divided into aplurality of regions. Herein, the feature maps are divided by using thesame dividing manner as that used during training.

In Step S1404, forward propagation is performed by putting the an objectto be classified into the convolutional-neural-network-based classifierto obtain an output result, and during the forward propagation, each ofthe convolutional templates, which correspond to the plurality ofregions respectively, is used for obtaining a response value of a neuronin the corresponding region. Preferably, the response value may becalculated by using an activation function configured to acquire apositive response value. For example, the activation function may be anactivation function configured to take an absolute value for a negativeconvolutional result or to convert a negative convolutional result to apositive small value.

According to the embodiment of the present invention, by improving aconvolution operation as a less-constrained convolution operation, it ismade possible to improve the flexibility of the neural networkclassifier, thereby achieving the object of extracting more features tothereby further improve recognition accuracy. In addition, by improvingthe existing activation functions as activation functions configured toacquire a positive response value, it is made possible to increase aconvergence speed.

The foregoing combined with specific embodiments describes the basicprinciple of the present invention. However, it should be noted that:those ordinarily skilled in the art would appreciate that all or anystep or component of the method and the apparatus of the presentinvention may be realized, in the form of hardware, firmware, softwareor combinations thereof, in any calculation means (including aprocessor, a storage medium, etc.) or a network of calculation means.This can be realized by those ordinarily skilled in the art by applyingtheir basic programming skills after reviewing the descriptions of thepresent invention.

Therefore, the object of the present invention may also be achieved byrunning a program or a set of programs on any calculation means. Thecalculation means may be well-known versatile means. Therefore, theobject of the present application may also be achieved by only providinga program product containing a program code for carrying out a method ormeans. That is, such a program product also constitutes the presentinvention, and a storage medium storing such a program product alsoconstitutes the present invention. Obviously, the storage medium may beany well-known storage medium or any storage medium that would bedeveloped in the future.

In carrying out the embodiment of the present invention by means ofsoftware and/or firmware, a program constituting the software isinstalled from the storage medium or network to a computer having adedicated hardware structure, e.g. the versatile computer 1500 as shownin FIG. 15, and the computer can perform various functions and so onwhen it is installed with various programs.

In FIG. 15, a central processing unit (CPU) 1501 performs variousprocessing according to a program stored in a Read Only Memory (ROM)1502 or a program loaded from a storage section 1508 to a Random AccessMemory (RAM) 1503. In the RAM 1503, data needed in performing variousprocessing by the CPU 1501 and so on is also stored according torequirements. The CPU 1501, the ROM 1502 and the RAM 1503 are linked toeach other via a bus 1504. An input/output interface 1505 is also linkedto the bus 1504.

The following components are linked to the input/output interface 1505:an inputting section 1506 (including a keyboard, a mouse, etc.), anoutputting section 1507 (including a display, such as a Cathode-Ray Tube(CRT), a Liquid Crystal Display (LCD) and so on, a loudspeaker, etc.), astorage section 1508 (including a hard disk, etc.), and a communicationsection 1509 (including a network interface card such as an LAN card, amodem and so on). The communication section 1509 performs communicationprocess via network such as Internet. The communication section 1509 mayalso be linked to the input/output interface 1505 as needed. Adetachable medium 1511 such as a magnetic disk, an optical disk, amagneto optical disk, a semiconductor memory and so on is installed on adriver 1511 according to requirements, such that a computer program readtherefrom is installed into the storage section 1508 according torequirements.

In carrying out the above series of processing by means of software, aprogram constituting software is installed from a network such asInternet or a storage medium such as the detachable medium 1511.

As would be understood by those skilled in the art, this storage mediumis not limited to the detachable medium 1511 as shown in FIG. 15 whichstores therein with a program and which is distributed separately fromdevices to provide a program for a user. Examples of the detachablemedium 1511 include a magnetic disk (including a floppy disk (registeredtrademark)), an optical disk (including a Compact Disk Read-Only Memory(CD-ROM) and a Digital Versatile Disk (DVD), a magneto optical disk(including a minidisk (MD) (a registered trademark)) and a semiconductormemory. Or, the storage medium may be the ROM 1502, a hard disk includedin the storage section 1508, etc., which store a program, and which aredistributed together with a device including them to the user.

The present invention further proposes a program product storing amachine-readable instruction code. When the instruction code is read andexecuted by a machine, the above method according to the embodiment ofthe present invention can be carried out.

Correspondingly, a storage medium for carrying the program productstoring a machine-readable instruction code is also included by thedisclosure of the present invention. The storage medium includes but isnot limited to a floppy disk, an optical disk, a magneto optical disk, amemory card, a memory stick, etc.

As would be readily understood by those ordinarily skilled in the art,the examples listed herein are only exemplary, and the present inventionis not limited thereto.

In the Description, expressions such “first”, “second” and “Nth” areused for distinguishing the described features in terms of wording, soas to clearly describe the present invention. Therefore, theseexpressions shall not be regarded as being limitative at all.

As an example, the respective steps of the above method and therespective constituent modules and/or units of the above device may beembodied as software, firmware, hardware or combinations thereof, andserve as a part of a corresponding device. Specific means or mannersthat can be used for configuring the respective constituent modules andunits in the above apparatus in the form of software, firmware, hardwareor combinations thereof are well-known to those skilled in the art,which will not be repeatedly described herein.

As an example, in carrying out the embodiment of the present inventionby means of software and/or firmware, a program constituting thesoftware is installed from the storage medium or network to a computerhaving a dedicated hardware structure, e.g. the versatile computer 1500as shown in FIG. 15, and the computer can perform various functions andso on when it is installed with various programs.

In the foregoing descriptions of the detailed embodiments of the presentinvention, features described and/or shown for one embodiment may beused in one or more other embodiments in an identical or similar manner,be combined with features in other embodiments, or substitute forfeatures in other embodiments.

It should be emphasized that when used in this text the term“comprise/include” refers to existence of features, elements, steps ofassemblies, without excluding existence or addition of one or more otherfeatures, elements, steps or assemblies.

In addition, the method according to the present invention is notlimited to be carried out in the temporal order described in theDescription, but may also be carried out in parallel or separately inother temporal orders. Therefore, the order of carrying out the methoddescribed in the Description fails to constitute a limitation to thetechnical scope of the present invention.

The present invention and the advantages thereof have been describedabove. However, it should be understood that various changes,substitutions or transformations may be carried out without departingfrom the scope of the present invention defined by the attached claims.In addition, the scope of the present invention is not limited only tothe detailed embodiments of processes, devices, means, methods and stepsdescribed in the Description. From the disclosure of the presentinvention, those ordinarily skilled in the art would readily understandthat, according to the present invention, processes, devices, means,methods or steps existing or to be developed in the future which performa substantially the same function or obtains a substantially the sameresult as the corresponding embodiments herein may be used. Therefore,the attached claims aim to embrace such processes, devices, means,methods or steps within the scopes thereof.

From the above descriptions, it can be seen that the disclosure at leastdiscloses the following technical solutions:

Technical solution 1. A convolutional-neural-network-based classifier,comprising:a plurality of feature map layers, at least one feature map in at leastone of feature maps being divided into a plurality of regions; anda plurality of convolutional templates corresponding to the plurality ofregions respectively, each of the convolutional templates being used forobtaining a response value of a neuron in the corresponding region.Technical solution 2. The convolutional-neural-network-based classifierof technical solution 1, wherein the convolutional-neural-network-basedclassifier is configured for classifying an image, and the image isdivided into a plurality of image regions, which correspond to theplurality of regions respectively, according to a fixed scheme.Technical solution 3. The convolutional-neural-network-based classifierof technical solution 1, wherein the convolutional-neural-network-basedclassifier is configured for classifying an image, and theconvolutional-neural-network-based classifier further comprises a imagedividing unit which is configured for dividing the image into aplurality of image regions corresponding to the plurality of regionsrespectively.Technical solution 4. The convolutional-neural-network-based classifierof technical solution 3, wherein the plurality of regions is dividedbased on a pixel distribution of the image. Technical solution 5. Theconvolutional-neural-network-based classifier of technical solution 4,wherein the plurality of regions is divided based on a quantitydistribution or a density distribution of foreground pixels in a binaryimage of the image.Technical solution 6. The convolutional-neural-network-based classifierof technical solution 3, wherein the plurality of regions is dividedbased on a structure of the image.Technical solution 7. The convolutional-neural-network-based classifierof any one of technical solutions 1 to 6, wherein for different featuremaps in the same feature map layer, or for feature maps in the differentfeature map layers, the different dividing schemes are used for dividingthe plurality of regions.Technical solution 8. The convolutional-neural-network-based classifierof any one of technical solutions 1 to 6, wherein an activation functionused for calculating the response value is configured for obtaining apositive response value.Technical solution 9. The convolutional-neural-network-based classifierof technical solution 8, wherein the activation function is configuredto take an absolute value for a negative convolutional result or toconvert a negative convolutional result to a positive small value.Technical solution 10. A classifying method by using aconvolutional-neural-network-based classifier which comprises aplurality of feature map layers, comprising:dividing at least one feature map in at least one of the plurality ofthe feature map layers into a plurality of regions;performing forward propagation by inputting an object to be classifiedinto the convolutional-neural-network-based classifier to obtain anoutput result, during the forward propagation, each of the convolutionaltemplates, which correspond to the plurality of regions respectively, isused for obtaining a response value of a neuron in the correspondingregion; and classifying the object to be classified according to theoutput result.Technical solution 11. The method of technical solution 10, wherein theobject to be classified is an image, the step of dividing at least onefeature map in at least one of the plurality of the feature map layersinto a plurality of regions comprising:dividing the image into a plurality of image regions, which correspondto the plurality of regions respectively.Technical solution 12. The method of technical solution 11, wherein theobject to be classified is an image, the step of dividing at least onefeature map in the at least one of the plurality of the feature maplayers into a plurality of regions comprising:dividing the image into a plurality of image regions, which correspondto the plurality of regions respectively, according to a fixed scheme.Technical solution 13. The method of technical solution 11, wherein thestep of dividing at least one feature map in at least one of theplurality of the feature map layers into a plurality of regionscomprises:dividing the at least one feature map into the plurality of regionsbased on a pixel distribution of the image.Technical solution 14. The method of technical solution 13, wherein thestep of dividing at least one feature map in at least one of theplurality of the feature map layers into a plurality of regionscomprises:dividing the at least one feature map into the plurality of regionsbased on a quantity distribution or a density distribution of foregroundpixels of a binary image of the image. Technical solution 15. The methodof technical solution 11, wherein the step of dividing at least onefeature map in at least one of the plurality of the feature map layersinto a plurality of regions comprises:dividing the at least one feature map into the plurality of regionsbased on the structure of the image.Technical solution 16. The method of any one of technical solutions 10to 15, wherein the step of dividing at least one feature map in at leastone of the plurality of the feature map layers into a plurality ofregions comprises:for different feature maps in the same layer of feature maps, or forfeature maps in the different layers of feature maps, using differentdividing schemes to dividing the plurality of regions.Technical solution 17. The method of any one of technical solutions 10to 15, wherein the step that each of the convolutional templates, whichcorrespond to the plurality of regions respectively, is used forobtaining a response value of a neuron in the corresponding regioncomprises:calculating the response value using an activation value configured toacquire a positive response value.Technical solution 18. The method of technical solution 17, wherein thestep that each of the convolutional templates, which correspond to theplurality of regions respectively, is used for obtaining a responsevalue of a neuron in the corresponding region comprises:calculating the response value using an activation value configured totake an absolute value for a negative convolutional result or to converta negative convolutional result to a positive small value.Technical solution 19. A method for training theconvolutional-neural-network-based classifier, which comprises aplurality of feature map layers, comprising: dividing at least onefeature map in at least one of the plurality of the feature map layersinto a plurality of regions;performing forward propagation by inputting a training sample with aknown flag into the convolutional-neural-network-based classifier toobtain an output result, during the forward propagation, each of theconvolutional templates, which correspond to the plurality of regionsrespectively, is used for obtaining a response value of a neuron in thecorresponding region; performing back propagation according to thedifference between the output result and the known flag to correctparameters of the convolutional-neural-network-based classifiercomprising the weights in the convolutional template; andrepeating the above steps until a predetermined condition is met.Technical solution 20. The method according to technical solution 19,wherein the training sample is an image, the step of dividing at leastone feature map in at least one of the plurality of the feature maplayers into a plurality of regions comprising:dividing the image into a plurality of images regions, which correspondto the plurality of regions respectively, according to a fixed schemerespectively, wherein the fixed scheme is determined based on generalfeatures of the training sample.Technical solution 21. The method according to technical solution 19,wherein the training sample is an image, the step of dividing at leastone feature map in at least one of the plurality of the feature maplayers into a plurality of regions comprising:dividing each image into a plurality of image regions, which correspondto the plurality of regions respectively, based on the personal featuresof each the image respectively. Technical solution 22. The methodaccording to technical solution 21, wherein the step of dividing atleast one feature map in at least one of the plurality of the featuremap layers into a plurality of regions comprises:dividing the at least one feature map into the plurality of regionsbased on a pixel distribution of the image.Technical solution 23. The method according to technical solution 22,wherein the step of dividing at least one feature map in at least one ofthe plurality of the feature map layers into a plurality of regionscomprises:dividing the at least one feature map into the plurality of regionsbased on a quantity distribution or a density distribution of foregroundpixels of a binary image of the image.Technical solution 24. The method according to technical solution 21,wherein the step of dividing at least one feature map in at least one ofthe plurality of the feature map layers into a plurality of regionscomprises:dividing the at least one feature map into the plurality of regionsbased on the structure of the image.Technical solution 25. The method of any one of technical solutions 19to 25, wherein the step of dividing at least one feature map in at leastone of the plurality of the feature map layers into a plurality ofregions comprises:for different feature maps in the same layer of feature maps, or forfeature maps in the different layers of feature maps, using differentdividing schemes for the dividing of the plurality of regions.Technical solution 26. The method of any one of technical solutions 19to 25, wherein the step that each of the convolutional templates, whichcorrespond to the plurality of regions respectively, is used forobtaining a response value of a neuron in the corresponding regioncomprises:calculating the response value by using an activation value configuredto acquire a positive response value.Technical solution 27. The method of technical solution 26, wherein thestep that each of the convolutional templates, which correspond to theplurality of regions respectively, is used for obtaining a responsevalue of a neuron in the corresponding region comprises:calculating the response value by using an activation value configuredto take an absolute value for a negative convolutional result or toconvert a negative convolutional result to a positive small value.

1. A convolutional-neural-network-based classifier, comprising: aplurality of feature map layers, at least one feature map in at leastone of feature maps being divided into a plurality of regions; and aplurality of convolutional templates corresponding to the plurality ofregions respectively, each of the convolutional templates being used forobtaining a response value of a neuron in the corresponding region. 2.The convolutional-neural-network-based classifier of claim 1, whereinthe convolutional-neural-network-based classifier is configured forclassifying an image, and the image is divided into a plurality of imageregions, which correspond to the plurality of regions respectively,according to a fixed scheme.
 3. The convolutional-neural-network-basedclassifier of claim 1, wherein the convolutional-neural-network-basedclassifier is configured for classifying an image, and theconvolutional-neural-network-based classifier further comprises a imagedividing unit which is configured for dividing the image into aplurality of image regions corresponding to the plurality of regionsrespectively.
 4. The convolutional-neural-network-based classifier ofclaim 3, wherein the plurality of regions is divided based on a pixeldistribution of the image.
 5. The convolutional-neural-network-basedclassifier of claim 4, wherein the plurality of regions is divided basedon a quantity distribution or a density distribution of foregroundpixels in a binary image of the image.
 6. Theconvolutional-neural-network-based classifier of claim 3, wherein theplurality of regions is divided based on a structure of the image. 7.The convolutional-neural-network-based classifier of claim 1, whereinfor different feature maps in the same feature map layer, or for featuremaps in the different feature map layers, the different dividing schemesare used for dividing the plurality of regions.
 8. Theconvolutional-neural-network-based classifier of claim 1, wherein anactivation function used for calculating the response value isconfigured for obtaining a positive response value.
 9. Theconvolutional-neural-network-based classifier of claim 8, wherein theactivation function is configured to take an absolute value for anegative convolutional result or to convert a negative convolutionalresult to a positive small value.
 10. A classifying method by using aconvolutional-neural-network-based classifier which comprises aplurality of feature map layers, comprising: dividing at least onefeature map in at least one of the plurality of the feature map layersinto a plurality of regions; performing forward propagation by inputtingan object to be classified into the convolutional-neural-network-basedclassifier to obtain an output result, during the forward propagation,each of the convolutional templates, which correspond to the pluralityof regions respectively, is used for obtaining a response value of aneuron in the corresponding region; and classifying the object to beclassified according to the output result.
 11. The method of claim 10,wherein the object to be classified is an image, the step of dividing atleast one feature map in at least one of the plurality of the featuremap layers into a plurality of regions comprising: dividing the imageinto a plurality of image regions, which correspond to the plurality ofregions respectively.
 12. The method of claim 11, wherein the object tobe classified is an image, the step of dividing at least one feature mapin the at least one of the plurality of the feature map layers into aplurality of regions comprising: dividing the image into a plurality ofimage regions, which correspond to the plurality of regionsrespectively, according to a fixed scheme.
 13. The method of claim 11,wherein the step of dividing at least one feature map in at least one ofthe plurality of the feature map layers into a plurality of regionscomprises: dividing the at least one feature map into the plurality ofregions based on a pixel distribution of the image.
 14. The method ofclaim 11, wherein the step of dividing at least one feature map in atleast one of the plurality of the feature map layers into a plurality ofregions comprises: dividing the at least one feature map into theplurality of regions based on the structure of the image.
 15. The methodof claim 10, wherein the step of dividing at least one feature map in atleast one of the plurality of the feature map layers into a plurality ofregions comprises: for different feature maps in the same layer offeature maps, or for feature maps in the different layers of featuremaps, using different dividing schemes to dividing the plurality ofregions.
 16. The method of claim 10, wherein the step that each of theconvolutional templates, which correspond to the plurality of regionsrespectively, is used for obtaining a response value of a neuron in thecorresponding region comprises: calculating the response value using anactivation value configured to acquire a positive response value.
 17. Amethod for training the convolutional-neural-network-based classifier,which comprises a plurality of feature map layers, comprising: dividingat least one feature map in at least one of the plurality of the featuremap layers into a plurality of regions; performing forward propagationby inputting a training sample with a known flag into theconvolutional-neural-network-based classifier to obtain an outputresult, during the forward propagation, each of the convolutionaltemplates, which correspond to the plurality of regions respectively, isused for obtaining a response value of a neuron in the correspondingregion; performing back propagation according to the difference betweenthe output result and the known flag to correct parameters of theconvolutional-neural-network-based classifier comprising the weights inthe convolutional template; and repeating the above steps until apredetermined condition is met.
 18. The method according to claim 17,wherein the training sample is an image, the step of dividing at leastone feature map in at least one of the plurality of the feature maplayers into a plurality of regions comprising: dividing the image into aplurality of images regions, which correspond to the plurality ofregions respectively, according to a fixed scheme respectively, whereinthe fixed scheme is determined based on general features of the trainingsample.
 19. The method according to claim 17, wherein the trainingsample is an image, the step of dividing at least one feature map in atleast one of the plurality of the feature map layers into a plurality ofregions comprising: dividing each image into a plurality of imageregions, which correspond to the plurality of regions respectively,based on the personal features of each the image respectively.
 20. Themethod according to claim 19, wherein the step of dividing at least onefeature map in at least one of the plurality of the feature map layersinto a plurality of regions comprises: dividing the at least one featuremap into the plurality of regions based on a pixel distribution of theimage.